共查询到20条相似文献,搜索用时 672 毫秒
1.
针对多传感器获取空中目标的多识别特征,提出了基于贝叶斯Noisy Or Gate网络的目标识别模型;该模型考虑未知因素的影响,将识别特征按二值节点进行网络识别结构构造,利用单个特征的识别结果,计算得到多个特征识别的任意组合,条件概率个数可以从2n减小为2n.仿真计算结果表明,该方法具有简化知识获取,节省存储空间,证据传播及时,实时性高的特点,为目标分类与识别提供了一个新的途径。 相似文献
2.
基于D-S理论的多传感器目标识别能力分析 总被引:1,自引:0,他引:1
通过分析待识别目标类型与结果类型的差别,确定以目标正确识别概率、目标错误识别概率和目标无法识别概率来度量传感器的目标识别能力。鉴于各传感器的识别结果概率之间的证据冲突,给出了一种通过与准理想结果的距离大小来确定信任权重的方法,并依据信任权重重新调整各证据的概率分配,实现冲突证据的预处理,然后使用Dempster规则对各传感器的识别结果概率进行组合,从而确定了多传感器的目标识别能力的度量模型,实例证明给出的目标识别能力度量方法是有效可行的。 相似文献
3.
4.
针对具有多个特征指标的多传感器目标识别问题,采用 Vague 集表达目标特征的不确定信息,提出了一种新的多传感器目标识别方法.定义两 Vague 集之间的加权 Hamming 距离和相似度,建立了Vague 集表达的多传感器目标识别模型,通过最小化各目标类型的 Vague 度优化模型客观地确定了各特征的权重,利用相似度... 相似文献
5.
6.
7.
基于加权D-S证据理论的分布式多传感器目标识别 总被引:1,自引:0,他引:1
针对分布式多传感器环境下的目标识别问题,提出了一种基于加权D-S证据理论组合规则的决策融合方法。分析了多传感器目标识别系统的信息模型,指出传感器的决策可信度由其被支持度及与目标间的距离确定。将该可信度体现为加权D-S证据理论组合规则中的证据权值,综合考虑传感器支持度及其与目标距离,给出了权值确定方法。仿真实验证明方法提高了融合效率,可较快完成识别任务。 相似文献
8.
提出了一种基于虚拟多传感器融合技术的红外目标识别方法.文中利用傅里叶描述器提取目标形状的边缘特征以及辐射特性的六个特征量,采用多个人工神经网络对来自单一传感器的目标利用不同特征分别识别,再利用D-S证据推理将各个网络的识别结果进行决策级融合.仿真实验结果表明,该方法提高了识别率和识别结果的可靠性. 相似文献
9.
10.
《电子制作.电脑维护与应用》2016,(2)
本文研究无线传感器网络多目标时应用了压缩感知,对建立该传感器的网络模型的过程进行了分析,阐述了传感器节点二维位置重构的算法,并使用MATLAB进行仿真,得知定位无线传感器网络节点时应用贝叶斯压缩感知的有效性。 相似文献
11.
There is strong anatomical and physiological evidence that neurons with large receptive fields located in higher visual areas are recurrently connected to neurons with smaller receptive fields in lower areas. We have previously described a minimal neuronal network architecture in which top-down attentional signals to large receptive field neurons can bias and selectively read out the bottom-up sensory information to small receptive field neurons (Hahnloser, Douglas, Mahowald, & Hepp, 1999). Here we study an enhanced model, where the role of attention is to recruit specific inter-areal feedback loops (e.g., drive neurons above firing threshold). We first illustrate the operation of recruitment on a simple example of visual stimulus selection. In the subsequent analysis, we find that attentional recruitment operates by dynamical modulation of signal amplification and response multistability. In particular, we find that attentional stimulus selection necessitates increased recruitment when the stimulus to be selected is of small contrast and of small distance away from distractor stimuli. The selectability of a low-contrast stimulus is dependent on the gain of attentional effects; for example, low-contrast stimuli can be selected only when attention enhances neural responses. However, the dependence of attentional selection on stimulus-distractor distance is not contingent on whether attention enhances or suppresses responses. The computational implications of attentional recruitment are that cortical circuits can behave as winner-take-all mechanisms of variable strength and can achieve close to optimal signal discrimination in the presence of external noise. 相似文献
12.
The representation of sound signals at the cochlea and auditory cortical level has been studied as an alternative to classical analysis methods. In this work, we put forward a recently proposed feature extraction method called approximate auditory cortical representation, based on an approximation to the statistics of discharge patterns at the primary auditory cortex. The approach here proposed estimates a non-negative sparse coding with a combined dictionary of atoms. These atoms represent the spectro-temporal receptive fields of the auditory cortical neurons, and are calculated from the auditory spectrograms of clean signal and noise. The denoising is carried out on noisy signals by the reconstruction of the signal discarding the atoms corresponding to the noise. Experiments are presented using synthetic (chirps) and real data (speech), in the presence of additive noise. For the evaluation of the new method and its variants, we used two objective measures: the perceptual evaluation of speech quality and the segmental signal-to-noise ratio. Results show that the proposed method improves the quality of the signals, mainly under severe degradation. 相似文献
13.
14.
Cortical sensory neurons are known to be highly variable, in the sense that responses evoked by identical stimuli often change dramatically from trial to trial. The origin of this variability is uncertain, but it is usually interpreted as detrimental noise that reduces the computational accuracy of neural circuits. Here we investigate the possibility that such response variability might in fact be beneficial, because it may partially compensate for a decrease in accuracy due to stochastic changes in the synaptic strengths of a network. We study the interplay between two kinds of noise, response (or neuronal) noise and synaptic noise, by analyzing their joint influence on the accuracy of neural networks trained to perform various tasks. We find an interesting, generic interaction: when fluctuations in the synaptic connections are proportional to their strengths (multiplicative noise), a certain amount of response noise in the input neurons can significantly improve network performance, compared to the same network without response noise. Performance is enhanced because response noise and multiplicative synaptic noise are in some ways equivalent. So if the algorithm used to find the optimal synaptic weights can take into account the variability of the model neurons, it can also take into account the variability of the synapses. Thus, the connection patterns generated with response noise are typically more resistant to synaptic degradation than those obtained without response noise. As a consequence of this interplay, if multiplicative synaptic noise is present, it is better to have response noise in the network than not to have it. These results are demonstrated analytically for the most basic network consisting of two input neurons and one output neuron performing a simple classification task, but computer simulations show that the phenomenon persists in a wide range of architectures, including recurrent (attractor) networks and sensorimotor networks that perform coordinate transformations. The results suggest that response variability could play an important dynamic role in networks that continuously learn. 相似文献
15.
16.
Statistically efficient processing schemes focus the resources of a signal processing system on the range of statistically probable signals. Relying on the statistical properties of retinal motion signals during ego-motion we propose a nonlinear processing scheme for retinal flow. It maximizes the mutual information between the visual input and its neural representation, and distributes the processing load uniformly over the neural resources. We derive predictions for the receptive fields of motion sensitive neurons in the velocity space. The properties of the receptive fields are tightly connected to their position in the visual field, and to their preferred retinal velocity. The velocity tuning properties show characteristics of properties of neurons in the motion processing pathway of the primate brain. 相似文献
17.
Steve Schneider Thai Son Hoang Ken Robinson Helen Treharne 《Electronic Notes in Theoretical Computer Science》2005,137(2):183
The introduction of probabilistic behaviour into the B-Method is a recent development. In addition to allowing probabilistic behaviour to be modelled, the relationship between expected values of the machine state can be expressed and verified. This paper explores the application of probabilistic B to a simple case study: tracking the volume of liquid held in a tank by measuring the flow of liquid into it. The flow can change as time progresses, and sensors are used to measure the flow with some degree of accuracy and reliability, modelled as non-deterministic and probabilistic behaviour respectively. At the specification level, the analysis is concerned with the expectation clause in the probabilistic B machine and its consistency with machine operations. At the refinement level, refinement and equivalence laws on probabilistic GSL are used to establish that a particular design of sensors delivers the required level of reliability. 相似文献
18.
The relative depth of objects causes small shifts in the left and right retinal positions of these objects, called binocular disparity. This letter describes an electronic implementation of a single binocularly tuned complex cell based on the binocular energy model, which has been proposed to model disparity-tuned complex cells in the mammalian primary visual cortex. Our system consists of two silicon retinas representing the left and right eyes, two silicon chips containing retinotopic arrays of spiking neurons with monocular Gabor-type spatial receptive fields, and logic circuits that combine the spike outputs to compute a disparity-selective complex cell response. The tuned disparity can be adjusted electronically by introducing either position or phase shifts between the monocular receptive field profiles. Mismatch between the monocular receptive field profiles caused by transistor mismatch can degrade the relative responses of neurons tuned to different disparities. In our system, the relative responses between neurons tuned by phase encoding are better matched than neurons tuned by position encoding. Our numerical sensitivity analysis indicates that the relative responses of phase-encoded neurons that are least sensitive to the receptive field parameters vary the most in our system. We conjecture that this robustness may be one reason for the existence of phase-encoded disparity-tuned neurons in biological neural systems. 相似文献
19.
一种带有色量测噪声的非线性系统辨识方法 总被引:2,自引:0,他引:2
利用最大似然判据, 本文提出了一种带有色量测噪声的非线性系统辨识方法. 首先, 利用量测差分方法将有色量测噪声白色化, 获得新的量测方程, 从而将带有色量测噪声的非线性系统辨识问题转化成带白色量测噪声和一步延迟状态的非线性系统辨识问题. 其次, 利用期望最大化(Expectation maximization, EM)算法提出了一种新的基于最大似然估计的非线性系统辨识方法, 该算法由期望步骤(Expectation step, E-step)和最大化步骤(Maximization step, M-step)两部分组成. 在期望步骤中, 基于当前估计的参数并利用带有色量测噪声的高斯近似滤波器和平滑器, 近似计算完整的对数似然函数的期望. 在最大化步骤中, 近似计算的似然函数期望值被最大化, 并且通过解析更新获得噪声参数估计, 通过Newton更新方法获得模型参数的估计. 最后, 数值仿真验证了本文提出算法的有效性. 相似文献
20.
Alexander Erreygers Jasper De Bock Gert de Cooman Arthur Van Camp 《国际强度与非线性控制杂志
》2019,29(12):3892-3914
》2019,29(12):3892-3914
One of the most basic problems in control theory is that of controlling a discrete‐time linear system subject to uncertain noise with the objective of minimizing the expectation of a quadratic cost. If one assumes the noise to be white, then solving this problem is relatively straightforward. However, white noise is arguably unrealistic: noise is not necessarily independent, and one does not always precisely know its expectation. We first recall the optimal control policy without assuming independence and show that, in this case, computing the optimal control inputs becomes infeasible. In the next step, we assume only the knowledge of lower and upper bounds on the conditional expectation of the noise and prove that this approach leads to tight lower and upper bounds on the optimal control inputs. The analytical expressions that determine these bounds are strikingly similar to the usual expressions for the case of white noise. 相似文献